Drought Prediction for Areas with Sparse Monitoring Networks: A Case Study for Fiji

Jinyoung Rhee,Hongwei Yang

doi:10.3390/w10060788

Abstract

Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized Precipitation Index for the case study area, Fiji. In order to overcome the limitation of a sparse monitoring network, both in-situ data and bias-corrected dynamic downscaling of historical climate data from the Weather Research Forecasting (WRF) model were used as reference data. Performance measures of the mean absolute error as well as classification accuracy were used. The WRF outputs reflect the topography of the area. Hybrid models showed better performance than simply bias corrected forecasts in most cases. Especially, the model based on Extra-Trees trained using the WRF model outputs performed the best in most cases.

Highlights

Islands in the South Pacific are vulnerable to climate change [1]
This study aims to develop a drought prediction model that can be used for areas with sparse monitoring networks
The performance of SPI6 predictions from bias-corrected precipitation forecast (FCST_ONLY hereafter) based on the same training dataset of the Weather Research Forecasting (WRF) model outputs was compared to the performance of ERT and Adaboost (Figure 4)

Summary

Introduction

Islands in the South Pacific are vulnerable to climate change [1]. The climate in the South Pacific has become drier by 15% and warmer by 0.8 ◦ C, compared to the earlier 20th century [2]. One of the key Pacific Island countries, experiences easterly trade winds on most calendar days. The easterly trade winds or the northeasterly monsoon, when lifted by high mountains, causes moisture condensation and produces heavy rainfall on the windward eastern side of Fiji. From a large-scale viewpoint, the El Nino Southern Oscillation (ENSO) is the main cause of climate variability over this region at interannual timescales. La Nina events dominated the interannual sea surface temperature (SST) anomaly (SSTA) over the central Equatorial Pacific during 1950 and 1975; after that time, El Nino events became more frequent [3].

Objectives

Methods

Results

Conclusion